Latent class analysis of medical mistrust and COVID-19 vaccine hesitancy among adults in the United States just prior to FDA emergency use authorization.

Using a nationally representative household sample, we sought to better understand types of medical mistrust as a driver of COVID-19 vaccine hesitancy. We used survey responses to conduct a latent class analysis to classify respondents into categories and explained this classification as a function of sociodemographic and attitudinal variables using multinomial logistic regression models. We then estimated the probability of respondents agreeing to receive a COVID-19 vaccine conditional on their medical mistrust category. We extracted a five-class solution to represent trust. The high trust group (53.0 %) is characterized by people who trust both their doctors and medical research. The trust in own doctor group (19.0 %) trust their own doctors but is ambiguous when it comes to trusting medical research. The high distrust group (6.3 %) neither trust their own doctor nor medical research. The undecided group (15.2 %) is characterized by people who agree on some dimensions and disagree on others. The no opinion group (6.2 %) did not agree nor disagree with any of the dimensions. Relative to the high trust group, those who trust their own doctors are almost 20 percentage points less likely to plan to get vaccinated (average marginal effect (AME) = 0.21, p <.001), and those who have high distrust are 24 percentage points less likely (AME = -0.24, p <.001) to report planning to get the vaccine. Results indicate that beyond sociodemographic characteristics and political attitudes, people's trust archetypes on parts of the medical field significantly predict their probability of wanting to get vaccinated. Our findings suggest that efforts to combat vaccine hesitancy should focus on building capacity of trusted providers to speak with their patients and parents of their patients, to recommend COVID-19 vaccination and build a trusting relationship; and increase trust and confidence in medical research.

Predictors of Mental Health among the General Population of U.S. Adults Eight Months into the COVID-19 Pandemic

Background: The COVID-19 pandemic has had profound impacts on mental health. We examined whether mental health differed based on sociodemographic and background characteristics, political party affiliation, and concerns about COVID-19. Methods: A cross-sectional, national sample of 1095 U.S. adults were surveyed October 22–26, 2020. The survey collected information on demographics, risk and protective behaviors for COVID-19, and mental health using the Mental Health Inventory-5 (MHI-5) scale. Independent samples t-tests, one-way Analysis of Variance tests, and a multivariable linear regression model were conducted. Results: Regression results showed respondents with criminal justice (B = −6.56, 95% CI = −10.05, −3.06) or opioid misuse (B = −9.98, 95% CI = −14.74, −5.23) histories reported poorer mental health than those without. Those who took protective behaviors (e.g., wearing masks) reported poorer mental health compared to those who indicated protective behaviors were unnecessary (B = 7.00, 95% CI = 1.61, 12.38) while those who took at least one risk behavior (e.g., eating in a restaurant) reported better mental health than those who did not. Conclusions: Our study shows that certain groups have experienced poorer mental health during the COVID-19 pandemic, suggesting that mental health should continue to be monitored so that public health interventions and messaging help prevent the spread of COVID-19 without increasing poor mental health outcomes.